Potential DNA barcodes for Melilotus species based on five single loci and their combinations.

RESEARCH ARTICLE

Potential DNA barcodes for Melilotus species based on five single loci and their combinations Fan Wu1, Jinxing Ma2, Yuqin Meng3, Daiyu Zhang1, Blaise Pascal Muvunyi1, Kai Luo1, Hongyan Di1, Wenli Guo1, Yanrong Wang1, Baochang Feng2*, Jiyu Zhang1*

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1 State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China, 2 National Quality Control & Inspection Centre for Grassland Industry Products, National Animal Husbandry Service, Ministry of Agriculture, Beijing, China, 3 China Agricultural Veterinarian Biology Science and Technology Co. Ltd, Lanzhou, China * [email protected] (BCF); [email protected] (JYZ)

Abstract OPEN ACCESS Citation: Wu F, Ma J, Meng Y, Zhang D, Pascal Muvunyi B, Luo K, et al. (2017) Potential DNA barcodes for Melilotus species based on five single loci and their combinations. PLoS ONE 12(9): e0182693. https://doi.org/10.1371/journal. pone.0182693 Editor: Shilin Chen, Chinese Academy of Medical Sciences and Peking Union Medical College, CHINA Received: May 6, 2017 Accepted: July 21, 2017 Published: September 14, 2017 Copyright: © 2017 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Melilotus, an annual or biennial herb, belongs to the tribe Trifolieae (Leguminosae) and consists of 19 species. As an important green manure crop, diverse Melilotus species have different values as feed and medicine. To identify different Melilotus species, we examined the efficiency of five candidate regions as barcodes, including the internal transcribed spacer (ITS) and two chloroplast loci, rbcL and matK, and two non-coding loci, trnH-psbA and trnLF. In total, 198 individuals from 98 accessions representing 18 Melilotus species were sequenced for these five potential barcodes. Based on inter-specific divergence, we analysed sequences and confirmed that each candidate barcode was able to identify some of the 18 species. The resolution of a single barcode and its combinations ranged from 33.33% to 88.89%. Analysis of pairwise distances showed that matK+rbcL+trnL-F+trnH-psbA+ITS (MRTPI) had the greatest value and rbcL the least. Barcode gap values and similarity value analyses confirmed these trends. The results indicated that an ITS region, successfully identifying 13 of 18 species, was the most appropriate single barcode and that the combination of all five potential barcodes identified 16 of the 18 species. We conclude that MRTPI is the most effective tool for Melilotus species identification. Taking full advantage of the barcode system, a clear taxonomic relationship can be applied to identify Melilotus species and enhance their practical production.

Data Availability Statement: All relevant data are within the paper and its Supporting Information files.

Introduction

Funding: Funded by National Basic Research Program (973) of China (2014CB138704), Special Fund for Agro-scientific Research in the Public Interest (20120304205), National Natural Science Foundation of China (31572453), the 111 project (B12002), State Key Laboratory of Grassland Agroecosystems (SKLGAE201702), and Program for

Melilotus (sweet clover), an annual or biennial herb, belongs to the tribe Trifolieae (Leguminosae) and consists of 19 species mainly distributed in North Africa and Eurasia [1]. These species can endure extreme environmental conditions, such as high salinity, drought and cold [1, 2], and have important medicinal value [3]. Coumarin is an important plant secondary metabolism compound found in Melilotus [4] and possesses a several antitumor activities, both preventing the occurrence of cancer and potentially curing cancer [5]. However, it is difficult to

PLOS ONE | https://doi.org/10.1371/journal.pone.0182693 September 14, 2017

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Potential DNA barcodes for Melilotus species based on five single loci and their combinations

Changjiang Scholars and Innovative Research Team in University (IRT_17R50). We thank the National Plant Germplasm System (NPGS, US) and National Gene Bank of Forage Germplasm (NGBFG, China) for providing experimental materials used in our study. Yanrong Wang, Baochang Feng and Jinxing Ma secured financial support. Yanrong Wang and Jiyu Zhang decided to publish the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: CO1, cytochrome c oxidase subunit 1; ITS, internal transcribed spacer; MR, matK +rbcL; MRP, matK+rbcL+trnH-psbA; MRPI, matK +rbcL+trnH-psbA+ITS; MRTP, matK+rbcL+trnL-F +trnH-psbA; MRTPI, matK+rbcL+trnL-F+trnH-psbA +ITS; NJ, Neighbour Joining; NPGS, National Plant Germplasm System; PCR, polymerase chain reactions; SSR, Simple sequence repeat.

assess the value of different species, as the coumarin content varies, with the highest (0.943%) in M. indicus accessions and none in M. segetalis accessions [6]. Among Melilotus species, characters of leaf, flower colour and structure, pod and seed present extensive variations [7], and although traditional classification methods can distinguish different Melilotus species, only experts and those with experience can accurately identify them. However, DNA barcodes can be used to rapidly identify different plants without extensive expertise. DNA barcode analysis examining one or several brief and standardized DNA fragment(s) [8, 9], allows for rapid, exact taxon discrimination. The primers utilized for DNA barcodes should be applied to the widest taxonomy, and standardized sequences 500–800 bp in length are used to distinguish among species from all eukaryotic kingdoms [10]. Ideally, a barcode region is stable, is particular to one species, and exhibits ample variation at the locus among species but little variation within a species. Thus, one can use such a sequence to unequivocally to identify closely related species [9, 11]. Although the universal barcode cytochrome c oxidase subunit 1 (CO1) is suitable for animals [9], it cannot be used as a barcode for plants given the extremely low mutation rate and unstable structure of the CO1 region in plant genomes [12]. So far, many studies have been taken in search of the universal plant barcode and several loci have been suggested as DNA barcodes in plants. These studies usually comprised wide taxonomic units, including Dicotyledonous plants, family of Fabaceae, tribe Trifolieae and so on. ITS2 region was used to identified dicotyledons, monocotyledons, gymnosperms and ferns, the success rates was different [13]. Fabaceae is a huge family, among them 91.3% of 104 Fabaceae medicinal species was identified successfully by using of trnH-psbA sequences [14]. Data from trnH-psbA region were analysed to illuminate molecular evolution of Maghrebian Medicago species and reveal high interspecific diversity and low intraspecific variation [15]. Three cpDNA regions (rbcL, trnH-psbA and matK) can distinguish Vachellia genera and discriminate sister-species among populations from Africa, Australia and India [16]. Besides, DNA barcodes were used to identify set of taxa characteristic for a certain region and analyzed as “local flora”. Costion and colleagues employed three types of barcodes (rbcLa, matK and trnHpsbA) to produce a DNA barcode reference library for Australian tropical plants [17]. Interphylogenetic information and evolutionary history of trees in Puerto Rico were obtained using three DNA barcodes (rbcL, matK and trnH-psbA) [18]. DNA barcodes were also applied to other different species. For the identification of medicinal plants, the internal transcribed spacer 2 (ITS2) sequence combined with the psbA-trnH sequence was recommended as one of most suitable DNA barcode [19, 20], and ITS2 was used to accurately distinguish medicinal plants in Artemisia [21]. Nuclear ITS sequence data can also be utilized to provide new information for identifying poisonous mushroom species [22] and to study the genetic diversity of M. albus and M. officinalis [23]. Previous researches have revealed that core barcodes, some combinations of potential barcodes, standard markers, and other sequences are not sufficiently reliable for DNA barcode development [24, 25]. Despite the broad applications of these markers, mutation rates of single-locus barcodes can be low, and certain regions, such as trnH-psbA, demonstrate amplification problems. Thus far, some multigene methods have been proposed to apply combinations of plastid regions that are relatively conservative as well as coding and non-coding fragments [26, 27]. In addition, the majority of studies at the genus level have only involved a small proportion of species [28, 29] and either concentrated on a single species [30] or focused on discovering a single universal barcode [10]. Therefore, standard barcodes for discriminating plant species are associated with several challenges and it is very difficult to reconcile with barcode universality. For the study of barcode, there are only few closely related species representing the same genus, rather than focus on species identification in the case of very closely related taxa. In our


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study, we concerned barcode analysis of Melilotus at the level of species. Regarding the 18 Melilotus species examined in this study, we are quite confident about the materials because we analysed 5 seed morphological traits and 9 agronomic traits for these germplasms [31, 32]. Moreover, 40 half-sib (HS) families of M. officinalis were obtained to evaluate genotypic variation as well as phenotypic and genotypic correlations [33]. Simple sequence repeat (SSR) analysis was performed to evaluate the genetic diversity of the 18 Melilotus species [34], and phylogenetic trees were constructed to study their inter-specific relationships [32]. No studies to date have measured the resolution of the DNA barcode system using numerous specimens covering almost all Melilotus species, and we established a standard DNA barcode system to assess the discrimination ability of each to propose the most powerful potential barcodes for Melilotus. The loci were selected based on the following two major criteria: a high level of species identification with broad coverage and a high-quality sequence. We used 201 individuals representing 18 Melilotus species to compare barcode performance for a nuclear locus (ITS), four plastid markers (trnH-psbA, trnL-F, matK, rbcL) and five combinations based on analysis of the barcode gap, similarity and pairwise distance.

Materials and methods Plant materials Seeds were selected from 98 accessions (S1 Table) representing 18 Melilotus species from Nation Plant Germplasm System (NPGS, USA) [32] and National Gene Bank of Forage Germplasm (NGBFG, China). Two to three accessions were selected to represent each of the Melilotus species. Prior to cultivation, seeds were gently polished and incubated at 24˚C for 16 and 8 hours of light and darkness, respectively. After 10 days of cultivation, 20 fresh seedlings from each accession were collected seperatly, frozen in liquid nitrogen and stored at -80˚C until assayed.

DNA extraction, amplification, and sequencing For each sample, total genomic DNA was extracted from whole seedlings using the SDS (sodium dodecyl sulfate) method [35]. Five pairs of primers, the internal transcribed spacer (ITS) [32], two chloroplast loci, rbcL [36] and matK [37], and two non-coding regions, trnHpsbA [38] and trnL-F [39], were amplified and sequenced. A standard polymerase chain reaction (PCR) in a volume of 25 μL was prepared as follows: 12.25 μL 2×Reaction Mix, 0.25 μL Golden DNA Polymerase, 2 μL each primer (10 μmol/mL), 6.5 μL ddH2O and 2 μL template genomic DNA (50 ng/mL). The PCR program was as follow: 94˚C for 3 min for pre-denaturation; 35 cycles of denaturation for 30 s at 94˚C, annealing for 30 s at 53˚C, and extension for 50 s at 72˚C, with the annealing temperature and extension time varying according to the different barcode genes, see Table 1; and a final extension for 7 min at 72˚C and a hold at 4˚C. Amplicons were sequenced by Shenggong Biotechnological, Ltd (Shanghai, China). Successfully sequenced samples were recorded.

Alignment Contigs were assembled and edited prior to alignment. The Contig Express module of Vector NTI Suite 6.0 software was used to remove both ends of the sequences and to keep the head and tail of the same gene at homologous sites. Sequences were aligned using DNAMAN6.0.

Single-barcode analyses Sequence alignment for the five DNA regions was performed using ClustalW of MEGA 6.0 software [40]. The Neighbour-Joining (NJ) method was used to generate a phylogenetic tree to


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Table 1. Information for PCR primers and amplification conditions used for five potential barcodes. Regin

ITS

Primer sequence(50 -30 )

Tm (˚C)

F:GGAAGKARAAGTCGTAACAAGG

Extension (s)

Aligned sequence length(bp)

The head of sequence

The end of sequence

53

50

646

CCAACACGTGAATCAGTTTGAACAC GGGGCTACCCGCTGAATTTAAGCAT

60

40

317

CTGCGGTCGAGGCTCCATCTATAAA TGGATTTGTGAATCCCCATGCGCGA

52

60

713

TTCATTTATTACGATTGTTTCTTTA CGATTTTTGCGAATATGCAGAATCT

53

55

431/642

CTTACCAAGTGAAAACTTTCAAATT CGGTAAAGCAAAGGACTGAAAATCC

53

50

754

CGCGCTCTACGTCTGGAAGATTTGC GTTCTGCCTGTTGCTTCGGGTGGTA

R:RGTTTCTTTTCCTCCGCTTA psbAtrnH matK

F:GTTATGCATGAACGTAATGCTC R:CGCGCATGGTGGATTCACAAATC F: CCCRTYCATCTGGAAATCTTGGTTC R: GCTRTRATAATGAGAAAGATTCTGC

trnL-F

F:CGAAATCGGTAGACGCTACG R:ATTTGAACTGGTGACACGAG

rbcL

F: AGACCTWTTTGAAGAAGGTTCWGT R:TCGGTYAGAGCRGGCATRTGCCA

ITS, internal transcribed spacer https://doi.org/10.1371/journal.pone.0182693.t001

obtain a pre-estimate of the discrimination ability of the five barcodes. The number of differences was used, and bootstrap values were calculated for 1000 replicates during construction of the NJ tree. Inter-specific genetic pairwise distances were calculated by Computing Pairwise Distance using MEGA 6.0 software. The candidate barcodes were classified on the basis of their identification ability. The sequences for each potential barcode were aligned among pairs, and the Emboss Needle algorithm (http://www.ebi.ac.uk/Tools/psa/emboss_needle/ nucleotide.html) was used to calculate the barcode gap value, score and similarity of each sequence. For comparison of gaps between accessions, 36 datasets were divided into two subsets according to species affinity, resulting in 18 sequences representing 18 species in each group.

Combination-barcode analysis Based on single candidate barcodes that were able to identify a few of 18 species, we assembled combinations of potential barcodes and obtained 195 combination sequences. These combinations included matK+rbcL (MR), matK+rbcL+trnH-psbA (MRP), matK+rbcL+trnH-psbA+ITS (MRPI), matK+rbcL+trnL+trnH-psbA (MRTP), and matK+rbcL+trnL+trnH-psbA+ITS (MRTPI). The combinations of each accession were assembled such that all sequences were connected end to end in the same order (Table 2). The same methods described above were used to assess the combination barcodes. Table 2. Results of comparison used to assess the utility of five potential barcodes. Universal primer

PCR success (%)

Sequencing success (%)

No. of SNPs

No. of indels

No. of species successfully identify

ITS

Yes

99

100

78

8

13

psbA-trnH

Yes

86

95

27

5

9

matK

Yes

100

99

26

0

9

trnL-F

Yes

100

87

25

3

12

rbcL

Yes

100

100

14

0

6

ITS, internal transcribed spacer https://doi.org/10.1371/journal.pone.0182693.t002


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Results Amplification, sequencing and alignment We primarily searched for primers that can successfully amplify the five chosen DNA regions of 18 Melilotus species. We utilized five universal primers (Table 1), amplifying a 646-bp sequence of ITS, a 317-bp sequence of trnH-psbA, a 713-bp sequence of matK, 431-bp and 462-bp sequences of trnL-F and a 754-bp sequence of rbcL. Except for trnH-psbA (86%), PCR amplification succeeded at rates of 99% to 100% among four of the potential barcodes. Moreover, the sequencing success rates ranged from 87% (trnL-F) to 100% (matK) (Table 2). A variation of 30 bp in length was noted in each barcode gene sequence evaluated. The highest number of single-nucleotide polymorphisms (SNPs) and indels was 78 and 8, respectively, for ITS; the lowest number of SNPs and indels was 14 and 0, respectively, for rbcL (Table 2). After editing and aligning the sequences, 18 pairs of standardized barcode sequences for each barcode system were obtained (S1 Dataset).

The gap and distance of accessions and individuals In total, 881 sequences (non-combinations) from 18 Melilotus species were obtained. Among those sequences, 100 individuals and 70 accessions were sequenced, with some success. For M. officinalis, the average gap means of 20 individuals for ITS, trnH-psbA, matK, trnl-F and rbcL were 0.000%, 0.029%, 0.148%, 0.000% and 0.000%, respectively. The average gaps for M. albus, M. latissimus, M. dentatus and M. spicatus ranged from 0.000% (ITS, trnH-psbA, matK, rbcL) to 1.083% (trnL-F) (S2 Table). The mean sequence distances of 20 individuals of M. officinalis were 0.63 (ITS), 0.25 (trnH-psbA), 1.28 (matK), 0.5 (trnl-F) and 0.00 (rbcL) (S2 Table). For accession sequences, both the gap and distance values were small: most of the gap and distance values were 0; the largest mean gap was 1.728% (M. wolgicus, trnH-psbA), and the largest mean distance was 4.02 (M. wolgicus, trnH-psbA) (S3 Table). After combining the five barcodes, intra-specific distances were found to be quite low, similar to the single-barcode values, with the majority of values being less than 1 (Fig 1A). The intra-species and intra-accession gap and distance values were similar to each other, with no apparent discrepancy. Because each value for the five barcodes was based on different subsamples and randomly selected samples representing nine species, we decreased the number of sequences to 200, which fully covered the five gene regions and the 18 Melilotus species to enable species resolution comparisons among the single barcodes and their combinations.

Single-barcode analysis For subsamples, the percentage of species resolution ranged from 72.22% for ITS to 33.33% for rbcL, with 66.67% (trnL), 52.94% (trnH-psbA) and 50.00% (matK) also observed (Fig 2). Regarding species identification, we divided the data into two subsets according to species affinity, with the following results: 13 different species were identified and gaps were similar for ITS, including M. altissimus (1.69% and 1.71%), M. dentatus (1.32% and 1.35%), M. indicus (1.85% and 1.94%) and M. infestus (2.99% and 2.83%) (Fig 3). Although a similar gap value was found for the remaining five species, the values were very close to each other. In contrast, for the trnL-F barcode, high gap values for M. altissimus (13.58% and 12.96%), M. dentatus (12.85% and 12.14%), M. indicus (24.15% and 16.78%), M. segetalis (16.45% and 12.81%), M. suaveolens (25.91% and 25.84%), and M. wolgicus (12.89% and 12.22%) did not allow for species identification (Fig 3). Either values that were too different for the same species or that


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Fig 1. Barcode gap analyses using distance histograms for five combination barcodes. The dataset of the sequences was obtained from MEGA 6.0 Compute Pairwise Distance. The histograms plots generated using R 3.2.3 display intraspecific and interspecific variation. A. Intraspecific pairwise distance, B. interspecific pairwise distance. MR, matK+rbcL; MRP, matK+rbcL+trnH-psbA; MRPI, matK+rbcL+trnH-psbA+ITS; MRTP, matK+rbcL+trnL-F +trnH-psbA; MRTPI, matK+rbcL+trnL-F+trnH-psbA+ITS. https://doi.org/10.1371/journal.pone.0182693.g001

were too similar were obtained. The result for trnH-psbA resembled those for trnL. Because the gap values of matK and rbcL were zero, similarity values were employed to compare the discrimination ability of each barcode (S1 Fig), with rbcL exhibiting the lowest species resolution. Comparison of the data set encompassing 36 specimens using the rbcL barcode indicated high similarity among the species. However, the percent species resolution for the matK barcode was slightly higher than that for the rbcL barcode (S1 Fig). Among the 18 species of

Fig 2. Percentages of species resolution for five single loci and their combinations. Specimens were analysed based on Neighbour-Joining in MEGA 6.0. The plots show the combinations of barcode loci surveyed on the x axis. I, ITS; M, matK; R, rbcL; P, trnH-psbA; T, trnL-F. https://doi.org/10.1371/journal.pone.0182693.g002


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Fig 3. Barcode gap valued for single loci based on the analysis of 36 sequences. A total of 36 sequences from 36 plant accessions representing 18 species were examined for every DNA barcode. The plots show marginally large sequence discrepancy for different species, with sequence differences from the same species being very slight. The error bars suggest 95% confidence intervals for the probability of correct identification (PCI) estimate. https://doi.org/10.1371/journal.pone.0182693.g003


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Melilotus investigated, the ITS barcode proved to be the most appropriate by successfully discriminating 13 species, whereas rbcL was the least efficient, discriminating only 6 of the 18 species.

Combination-barcode analysis We performed several analyses to achieve a direct comparison of the identification ability of the five loci by analysing single and combination barcodes. We found that the combination of the five loci in a multi-gene trial was able to rapidly enhance species discrimination. Overall, matK+rbcL (MR) identified 61.11% of the species. Adding a non-coding region (psbA) to this combination increased the resolution to 66.67% (MRP), the same value observed for matK+rbcL+trnL-F+trnH-psbA (MRPT), and the value for MRPI was even greater, at 83.33%. Moreover, 88.89% resolution, the best for discriminating among the species, was found with inclusion of all five gene regions (Fig 2). Combining the pairwise distances of the five combined potential barcodes using at least two specimens for each was able to discriminate the 18 Melilotus species. The maximum and minimum interspecific distances were determined for all of the species (S4 Table). With the exception of M. indicus (7.34) and M. infestus (7.06), which were marginally lower than MRPT (7.54 and 7.24, respectively), MRTPI showed the largest pairwise distance. However, MR consistently outperformed MRP, even though the MR D-value was very small. For the five multigene combinations, MRPI values were intermediate and slightly less than those of MRTP yet significantly greater than those of MRP. Overall, the average pairwise similarity analyses (S4 Table) were not consistent with the pairwise distance. The five combinations were ranked from the most powerful to the least effective with regard to their ability to detect similarities among the species: MR>MRP>MRPI>MRTPI>MRTP. In the multi-locus combinations, analysis of the frequency of pairwise comparisons was used to confirm the same trend in the maximum interspecific distance analysis (Fig 1B and S4 Table). The results revealed the highest frequency for MRTPI among the five combinations. The value for MRTP was followed by that for MRTPI. MR and MRP exhibited the lowest performance, despite the similar highest frequency value for each. The value of the largest frequency for MRPI was between those for MRP and MRTP (Fig 1B).

Species identification analysis Analysis based on the pairwise distance within single and multigene barcodes was performed to generate box plots for all possible interspecific pairwise distances within each barcode. As shown in Fig 4, the biggest median value (slightly > 5) was for MRTPI. MRTP and MRPI were more effective than ITS and trnL-F, though the median value for all of these four barcodes was nearly four. MR and MRP exhibited a similar median value of approximately of 2.5. Moreover, matK and trnH-psbA performed similarly, with smaller values (close to 2). RbcL proved to be consistently the least effective marker. The variation among the pairwise differences within each barcode followed different patterns. Further analysis of the barcode gap suggested the order of trnL-F>MRTP>MRTPI>trnHpsbA>MRPI>MRP>ITS>MR>matK for Melilotus (Fig 5). Ten candidate barcodes were analysed for their ability to discriminate 18 Melilotus species, with similar trends observed (S2 Fig). Considering all species, the largest gap value (with high marginal error) was found for trnL-F. The most powerful potential barcode was MRTPI, followed closely by MRTP. The same trend was observed for all species, except for M. italicus, which showed a higher gap value for MRP (0.17) than that for MRPI (0.13). Moreover, the trnH-psbA gap value for M. italicus was zero. The rbcL barcode exhibited the lowest identification power among all species,


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Fig 4. Interspecific pairwise distances analysis for each candidate barcode. All sequences of barcodes from this study were analysed with R 3.2.3. The analysis was performed to generate box plots for all possible interspecific pairwise distances for each barcode. The box plots show the combinations of barcode loci surveyed on the x axis. I, ITS; M, matK; R, rbcL; P, trnH-psbA; T, trnL-F. The median is the line in the middle of the box, the upper and lower regions of the box are the 25th and 75th percentiles, respectively, and the whiskers are 1.5 times the interquartile range above and below the box limits. The dots represent outliers. https://doi.org/10.1371/journal.pone.0182693.g004

with a value that was consistently zero. matK and MR performed poorly, as each was short of a marked barcode gap. Analysis of the barcode gap along with the score indicated a tendency toward an inverse relationship.

Discussion Sweet clover, valued for medicinal properties and used as animal feed, consists of 19 species mainly distributed in North and Eurasia [1]. Some species of Melilotus have entomophilous flowers that can result in hybridization, and it is therefore challenging to distinguish similar morphological characters among species and closely related species. Thus, a main objective of the current study was to the measure resolution rate of five potential barcodes and their combinations at the species level. Previously, trnH-psbA is reportedly a good DNA barcode for some plants, such as orchids [41] and Tetrastigma (Miq.) Planch [42]. RbcL and matK have been proposed as plant barcodes


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Fig 5. The average gap value for ten barcode systems. The plots show the combinations of barcode loci surveyed on the y axis. I, ITS; M, matK; R, rbcL; P, trnH-psbA; T, trnL-F. The x axis shows the barcode gap value for Melilotus genus. The error bars suggest 95% confidence intervals for the barcode gap estimate. https://doi.org/10.1371/journal.pone.0182693.g005

by the CBOL Plant Working Group [25] and have also been used to discriminate species of diverse plant taxa and to clarify taxonomic origins [43, 44] in many extensive taxonomic investigations. In our study, we found that ITS was to be the best candidate barcode among the five single loci (Fig 2). Similar results have been reported in Chlorophyta [45] as well as higher plants [28, 46, 47]. However, the ITS region of Ficus carica was the locus with the lowest resolution (25.57%) but the highest variation rate (0.0188) [48], contrary to the findings of Roy who


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reached the best species resolution of the Ficus genus with this barcode [49]. ITS as a barcode exhibits high levels of inter-specific and inter-individual sequence variation because of its multi-copy nature [50]. It has been reported that ITS identification rates are the lowest and that the discrimination rate of different barcodes (ITS, rbcL, matK, psbA-trnH) ranges from 12.21% to 25.19% in Rhododendron [51]. Regarding performance in species identification, the PCR amplification success rate and the sequencing success rate of ITS were very high (Table 2). Few problems in alignment or editing were observed for ITS, and the resolution far surpassed that of rbcL, matK and trnH-psbA and slightly exceeded that of trnL-F (Fig 2). In general, such types of markers have greater species-resolving power; regardless, there are limitations for ITS as a standard barcode for some taxa because of amplification and sequencing difficulties [28, 42]. in addition, for a number of slowly evolving groups, genetic drift could hamper ancestral polymorphism lineage sorting [10]. Sample parameters were compared and analysed, and the results suggested that each candidate barcode was able to identify a few of the 18 species, especially ITS can identify the most of 18 species (Fig 2). As there are a series of obstacles for single markers, a suitable solution is to employ more than one marker in combination [52]. Our analysis of two-, three-, four-, or fivelocus barcode systems (S4 Table) showed that MR possessed the lowest resolution, which was even lower than the single markers (ITS or trnL-F). However, a two-marker combination has been proposed before [53, 54], and the rbcL+matK barcode system was able to identify 93.1% of taxa sampled from local flora [55]. A plant barcode using rbcL+trnH-psbA was also applied to build a library containing over 700 species of the world’s medicinal plants [56]. Although the worst identification rate of Melilotus species occurred with the combination of rbcL and matK, the resolution of the assembled barcode system surpassed that of the individual barcodes. The other combinations exhibited the same patterns (Fig 2). It is reported that the combination of ITS + trnH-psbA could enhance discrimination at 90% greater power than that of the single barcode (ITS, 70%) [57]. In Rhododendron, the combination of ITS+psbA-trnH +matK or ITS+psbA-trnH+matK+rbcL showed the highest identification rate (41.98%), far greater than a single barcode (the highest value was 25.19%) [51]. We compared the entire barcoding system performance and analysed all parameters and found that simultaneous use of the five loci was ideal for discriminating between different Melilotus species. A most remarkable result indicated greater resolution than with single markers, regardless of the ones combined. In general, there are problems that limit the resolving power of barcodes, especially for chloroplast markers. Even when using all of the five barcodes together, there are small part species were identified unsuccessfully (Fig 2). Such problems can be attributed to complexity arising from reproductive and evolutionary behaviour, such as hybridization, polyploidization and mixture of sexual and asexual reproduction [47, 58]. Furthermore, in evolutionary history, introgression, reticulate evolution and incomplete lineage sorting may blur species boundaries, leading to impediments in clear barcoding [38, 59]. However, due to hybridization, it is difficult to mutate nucleotide sequences with higher conservation and synonymous substitution rates [60]. The nuclear ITS region is regarded as a core marker for identifying poisonous mushrooms [59], and our analysis confirmed that ITS is indeed a powerful potential barcode. In conclusion, as for the whole barcode system to identify Melilotus, ITS was the best single candidate barcode and the assembly of five loci was the best combination candidate barcode. In addition, 18 standard barcode sequences were established for each type of barcode system in the current study, and these barcodes can be used to identify unknown Melilotus plants. Because hybridization and mutation always occur, discovery of novel biodiversity and efficient barcodes requires well-coordinated initiatives.


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Supporting information S1 Fig. Similarity value for single locus based on the analysis of 36 sequences. 36 sequences from 36 plant accessions representing 18 species for every DNA barcode. The plots show that sequences discrepancy of different species is marginally large and difference of sequences from the same species somehow is very slight. The error bars suggest 95% confidence intervals for the PCI estimate. (TIF) S2 Fig. Gap value for identification of ten barcode systems in 18 Melilotus species. The plots show the combinations of barcode loci surveyed on the y axis. I, ITS; M, matK; R, rbcL; P, trnH-psbA; T, trnL-F. The x axis shows the barcode gap value for 18 species of Melilotus. The error bars suggest 95% confidence intervals for the barcode gap estimate. (TIF) S1 Table. Information for 98 accessions of 18 Melilotus species. (XLSX) S2 Table. Basic information and intraspecific gap and distance for 101 accessions of 18 Melilotus species. ITS, internal transcribed spacer. (XLSX) S3 Table. Intra-accession gap and distance for 100 individuals of 5 Melilotus species. ITS, internal transcribed spacer. (XLSX) S4 Table. Results of pairwise distance analysis based on MEGA 6.0-Compute Pairwise Distance and similarity analysis based on Emboss Needle. MR, matK+rbcL; MRP, matK+rbcL +trnH-psbA; MRPI, matK+rbcL+trnH-psbA+ITS; MRTP, matK+rbcL+trnL+trnH-psbA; MRTPI, matK+rbcL+trnL+trnH-psbA+ITS. (XLSX) S1 Dataset. Standardized barcode sequences of 18 Melilotus species for matK, rbcL, trnL, trnH-psbA and ITS. (ZIP)

Acknowledgments This work was supported by National Basic Research Program (973) of China (2014CB138704), Special Fund for Agro-scientific Research in the Public Interest (20120304205), National Natural Science Foundation of China (31572453), the 111 project (B12002), State Key Laboratory of Grassland Agro-ecosystems (SKLGAE201702), and Program for Changjiang Scholars and Innovative Research Team in University (IRT_17R50). Additionally, we thank the National Plant Germplasm System (NPGS, US) and National Gene Bank of Forage Germplasm (NGBFG, China) for providing experimental materials used in our study. We also thank the reviewers for reviewing our manuscript.

Author Contributions Conceptualization: Yanrong Wang, Jiyu Zhang. Data curation: Jinxing Ma, Blaise Pascal Muvunyi. Formal analysis: Blaise Pascal Muvunyi.


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Funding acquisition: Jinxing Ma, Yuqin Meng, Baochang Feng, Jiyu Zhang. Investigation: Yanrong Wang, Baochang Feng. Methodology: Kai Luo. Project administration: Daiyu Zhang, Kai Luo, Wenli Guo. Resources: Yuqin Meng, Daiyu Zhang, Wenli Guo. Software: Hongyan Di. Supervision: Hongyan Di. Writing – original draft: Fan Wu. Writing – review & editing: Fan Wu.

References 1.

Aboel-Atta A. Isozymes, RAPD and ISSR variation in Melilotus indica (L.) All. and M. siculus (Turra) BG Jacks.(Leguminosae). Int J Plant Sci. 2009; 2:113–8.

2.

Rogers M, Colmer T, Frost K, Henry D, Cornwall D, Hulm E, et al. Diversity in the genus Melilotus for tolerance to salinity and waterlogging. Plant Soil. 2008; 304(1–2):89–101. https://doi.org/10.1007/s11104007-9523-y

3.

Cong JM, Chen FQ, Sun CL. Study on comprehensive development of Metlilotus suaverolens. Journal of Anhui Agricultural Sciences. 2012; 5:155.

4.

Evans P, Kearney G. Melilotus albus (Medik.) is productive and regenerates well on saline soils of neutral to alkaline reaction in the high rainfall zone of south-western Victoria. Anim Prod Sci. 2003; 43 (4):349–55. https://doi.org/10.1071/EA02079

5.

Musa MA, Cooperwood JS, Khan MOF. A review of coumarin derivatives in pharmacotherapy of breast cancer. Curr Med Chem. 2008; 15(26):2664. https://doi.org/10.2174/092986708786242877 PMID: 18991629

6.

Nair RM, Whittall A, Hughes SJ, Craig AD, Revell DK, Miller SM, et al. Variation in coumarin content of Melilotus species grown in South Australia. New Zealand Journal of agricultural.

7.

Moussavi S. Species of Melilotus in Iran (key to the species, descriptions and their distributions). Rostaniha. 2001; 2(1/4).

8.

Floyd R, Abebe E, Papert A, Blaxter M. Molecular barcodes for soil nematode identification. Mol Ecol. 2002; 11(4):839–50. https://doi.org/10.1046/j.1365-294X.2002.01485.x PMID: 11972769

9.

Hebert PD, Cywinska A, Ball SL. Biological identifications through DNA barcodes. P Roy Soc Lond B Bio. 2003; 270(1512):313–21. https://doi.org/10.1098/rspb.2002.2218 PMID: 12614582

10.

Schoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL, Levesque CA, et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. PNAS. 2012; 109 (16):6241–6. https://doi.org/10.1073/pnas.1117018109 PMID: 22454494

11.

Hebert PD, Ratnasingham S, deWaard JR. Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. P Roy Soc Lond B Bio. 2003; 270(Suppl 1):S96–S9. https://doi. org/10.1098/rsbl.2003.0025 PMID: 12952648

12.

Cho Y, Mower JP, Qiu YL, Palmer JD. Mitochondrial substitution rates are extraordinarily elevated and variable in a genus of flowering plants. PNAS. 2004; 101(51):17741–6. https://doi.org/10.1073/pnas. 0408302101 PMID: 15598738

13.

Yao H, Song J, Liu C, Luo K, Han J, Li Y, et al. Use of ITS2 region as the universal DNA barcode for plants and animals. PLoS ONE. 2010; 5(10):e13102. https://doi.org/10.1371/journal.pone.0013102 PMID: 20957043

14.

Gao T, Ma X, Zhu X. Use of the psbA-trnH region to authenticate medicinal species of Fabaceae. Biological and Pharmaceutical Bulletin. 2013; 36(12):1975–9. https://doi.org/10.1248/bpb.b13-00611 PMID: 24432382

15.

Nadia Z, Maroua G, Hela BR, Houda CK, Abdelmajid H, Neila TF, et al. Evolutionary and demographic history among Maghrebian Medicago species (Fabaceae) based on the nucleotide sequences of the chloroplast DNA barcode trnH-psbA. Biochem Syst Ecol. 2014; 55:296–304. https://doi.org/10.1016/j. bse.2014.03.016


13 / 16


16.

Steven GN, Subramanyam R. Testing plant barcoding in a sister species complex of pantropical Acacia (Mimosoideae, Fabaceae). Mol Ecol Resour. 2009; 9(s1):172–80. https://doi.org/10.1111/j.1755-0998. 2009.02642.x PMID: 21564976

17.

Costion C, Lowe A, Rossetto M, Kooyman R, Breed M, Ford A, et al. Building a plant DNA barcode reference library for a diverse tropical flora: an example from queensland, Australia. Diversity. 2016; 8 (1):5. https://doi.org/10.3390/d8010005

18.

Muscarella R, Uriarte M, Erickson DL, Swenson NG, Zimmerman JK, Kress WJ. A well-resolved phylogeny of the trees of Puerto Rico based on DNA barcode sequence data. PLoS ONE. 2014; 9(11): e112843. https://doi.org/10.1371/journal.pone.0112843 PMID: 25386879

19.

Li X, Yang Y, Henry RJ, Rossetto M, Wang Y, Chen S. Plant DNA barcoding: from gene to genome. Biol Rev. 2015; 90(1):157–66. https://doi.org/10.1111/brv.12104 PMID: 24666563

20.

Dong R, Dong D, Luo D, Zhou Q, Chai X, Zhang J, et al. Transcriptome Analyses Reveal Candidate Pod Shattering-Associated Genes Involved in the Pod Ventral Sutures of Common Vetch (Vicia sativa L.). Front Plant Sci. 2017; 8. https://doi.org/10.3389/fpls.2017.00649 PMID: 28496452

21.

Shi S, Nan L, Smith K. The Current Status, Problems, and Prospects of Alfalfa (Medicago sativa L.) Breeding in China. Agron. 2017; 7(1):1. https://doi.org/10.3390/agronomy7010001

22.

Parnmen S, Sikaphan S, Leudang S, Boonpratuang T, Rangsiruji A, Naksuwankul K. Molecular identification of poisonous mushrooms using nuclear ITS region and peptide toxins: a retrospective study on fatal cases in Thailand. J Toxicol Sci. 2016; 41:65–76. https://doi.org/10.2131/jts.41.65 PMID: 26763394

23.

Di HY, Luo K, Zhang DY, Duan Z, Huo YX, Wang YR. Genetic diversity analysis of Melilotus populations based on ITS and trnL-trnF sequences. Acta Botanica Boreali-Occidentalia Sinica. 2014:0265–9.

24.

Kress WJ, Wurdack KJ, Zimmer EA, Weigt LA, Janzen DH. Use of DNA barcodes to identify flowering plants. PNAS. 2005; 102(23):8369–74. https://doi.org/10.1073/pnas.0503123102 PMID: 15928076

25.

Hollingsworth PM, Forrest LL, Spouge JL, Hajibabaei M, Ratnasingham S, van der Bank M, et al. A DNA barcode for land plants. PNAS. 2009; 106(31):12794–7. https://doi.org/10.1073/pnas. 0905845106 PMID: 19666622

26.

Fazekas AJ, Burgess KS, Kesanakurti PR, Graham SW, Newmaster SG, Husband BC, et al. Multiple multilocus DNA barcodes from the plastid genome discriminate plant species equally well. PLoS ONE. 2008; 3(7):e2802. https://doi.org/10.1371/journal.pone.0002802 PMID: 18665273

27.

Kress WJ, Erickson DL, Jones FA, Swenson NG, Perez R, Sanjur O, et al. Plant DNA barcodes and a community phylogeny of a tropical forest dynamics plot in Panama. PNAS. 2009; 106(44):18621–6. https://doi.org/10.1073/pnas.0909820106 PMID: 19841276

28.

Zhang DQ, Duan LZ, Zhou N. Application of DNA barcoding in Roscoea (Zingiberaceae) and a primary discussion on taxonomic status of Roscoea cautleoides var. pubescens. Biochem Syst Ecol. 2014; 52:14–9. https://doi.org/10.1016/j.bse.2013.10.004

29.

Guo X, Simmons MP, But PPH, Shaw PC, Wang RJ. Application of DNA barcodes in Hedyotis L.(Spermacoceae, Rubiaceae). J Syst Evol. 2011; 49(3):203–12. https://doi.org/10.1111/j.1759-6831.2011. 00130.x

30.

Xue CY, Li DZ. Use of DNA barcode sensu lato to identify traditional Tibetan medicinal plant Gentianopsis paludosa (Gentianaceae). J Syst Evol. 2011; 49(3):267–70. https://doi.org/10.1111/j.1759-6831. 2011.00127.x

31.

Luo K, Di HY, Zhang JY, Wang YR, Li ZQ. Preliminary evaluation of agronomy and quality traits of nineteen Melilotus accessions. Pratacultural Science. 2014; 31(11):2125–34.

32.

Di HY, Duan Z, Luo K, Zhang DY, Wu F, Zhang JY, et al. Interspecific phylogenic relationships within genus Melilotus based on nuclear and chloroplast DNA. PLoS ONE. 2015; 10(7):e0132596. https://doi. org/10.1371/journal.pone.0132596 PMID: 26167689

33.

Gong XY, Schaufele R, Schnyder H. Bundle-sheath leakiness and intrinsic water use efficiency of a perennial C4 grass are increased at high vapour pressure deficit during growth. J Exp Bot. 2016. Epub 2016/11/20. https://doi.org/10.1093/jxb/erw417 PMID: 27864539

34.

Wu F, Zhang DY, Ma JX, Luo K, Di HY, Liu ZP, et al. Analysis of genetic diversity and population structure in accessions of the genus Melilotus. Ind Crop Prod. 2016; 85:84–92. https://doi.org/10.1016/j. indcrop.2016.02.055

35.

Shan Z, Wu HL, Li CL, Chen H, Wu Q. Improved SDS method for general plant genomic DNA extraction. Guangdong Agricultural Sciences. 2011; 38(8):113–5.

36.

Dong W, Cheng T, Li C, Xu C, Long P, Chen C, et al. Discriminating plants using the DNA barcode rbcLb: an appraisal based on a large data set. Mol Ecol Resour. 2014; 14(2):336–43. https://doi.org/10. 1111/1755-0998.12185 PMID: 24119263


14 / 16


37.

Yu J, Xue JH, Zhou SL. New universal matK primers for DNA barcoding angiosperms. J Syst Evol. 2011; 49(3):176–81. https://doi.org/10.1111/j.1759-6831.2011.00134.x

38.

Chen SL, Zhang JQ, Meng SY, Wen J, Rao GY. DNA Barcoding of Rhodiola (Crassulaceae): A Case Study on a Group of Recently Diversified Medicinal Plants from the Qinghai-Tibetan Plateau. PLoS ONE. 2015; 10(3):1903–10. https://doi.org/10.1371/journal.pone.0119921 PMID: 25774915

39.

Taberlet P, Gielly L, Pautou G, Bouvet J. Universal primers for amplification of three non-coding regions of chloroplast DNA. Plant Mol Biol. 1991; 17(5):1105–9. https://doi.org/10.1007/BF00037152 PMID: 1932684

40.

Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol Biol Evol. 2013; 30(12):2725–9. https://doi.org/10.1093/molbev/mst197 PMID: 24132122

41.

Nitta JH. Exploring the utility of three plastid loci for biocoding the filmy ferns (Hymenophyllaceae) of Moorea. Taxon. 2008; 57(3):725–736.

42.

Fu YM, Jiang WM, Fu CX. Identification of species within Tetrastigma (Miq.) Planch.(Vitaceae) based on DNA barcoding techniques. J Syst Evol. 2011; 49(3):237–45. https://doi.org/10.1111/j.1759-6831. 2011.00126.x

43.

Chao Z, Zeng WP, Liao J, Liu L, Liang ZB, Li XL. DNA barcoding Chinese medicinal Bupleurum. Phytomedicine. 2014; 21(13):1767–73. https://doi.org/10.1016/j.phymed.2014.09.001 PMID: 25444445

44.

Guo XR, Wang XG, Su WH, Zhang GF, Zhou R. DNA barcodes for discriminating the medicinal plant Scutellaria baicalensis (Lamiaceae) and its adulterants. Biol Pharm Bull. 2011; 34(8):1198–203. PMID: 21804206

45.

Buchheim MA, Keller A, Koetschan C, Fo¨rster F, Merget B, Wolf M. Internal transcribed spacer 2 (nu ITS2 rRNA) sequence-structure phylogenetics: towards an automated reconstruction of the green algal tree of life. PLoS ONE. 2011; 6(2):e16931. https://doi.org/10.1371/journal.pone.0016931 PMID: 21347329

46.

Group CPB, Li DZ, Gao LM, Li HT, Wang H, Ge XJ, et al. Comparative analysis of a large dataset indicates that internal transcribed spacer (ITS) should be incorporated into the core barcode for seed plants. PNAS. 2011; 108(49):19641–6. https://doi.org/10.1073/pnas.1104551108 PMID: 22100737

47.

Zuo YJ, Chen ZJ, Kondo K, Funamoto T, Wen J, Zhou SL. DNA barcoding of Panax species. Planta Med. 2011; 77(2):182. https://doi.org/10.1055/s-0030-1250166 PMID: 20803416

48.

Castro C, Hernandez A, Alvarado L, Flores D. DNA barcodes in Fig cultivars (Ficus carica L.) using ITS regions of ribosomal DNA, the psbA-trnH spacer and the matK coding sequence. American Journal of Plant Sciences. 2015; 6(01):95. https://doi.org/10.4236/ajps.2015.61011

49.

Roy S, Tyagi A, Shukla V, Kumar A, Singh UM, Chaudhary LB, et al. Universal plant DNA barcode loci may not work in complex groups: a case study with Indian Berberis species. PLoS ONE. 2010; 5(10): e13674. https://doi.org/10.1371/journal.pone.0013674 PMID: 21060687

50.

Yamaguchi A, Kawamura H, Horiguchi T. A further phylogenetic study of the heterotrophic dinoflagellate genus, Protoperidinium (Dinophyceae) based on small and large subunit ribosomal RNA gene sequences. Phycol Res. 2006; 54(4):317–29. https://doi.org/10.1111/j.1440-1835.2006.00438.x

51.

Yan LJ, Liu J, Mo¨ller M, Zhang L, Zhang XM, Li DZ, et al. DNA barcoding of Rhododendron (Ericaceae), the largest Chinese plant genus in biodiversity hotspots of the Himalaya–Hengduan Mountains. Mol Ecol Resour. 2015; 15(4):932–44. https://doi.org/10.1111/1755-0998.12353 PMID: 25469426

52.

Bellemain E, Carlsen T, Brochmann C, Coissac E, Taberlet P, Kauserud H. ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC Microbiol. 2010; 10 (1):189. https://doi.org/10.1186/1471-2180-10-189 PMID: 20618939

53.

Rubinoff D, Cameron S, Will K. Are plant DNA barcodes a search for the Holy Grail? Trends Ecol Evol. 2006; 21(1):1–2. https://doi.org/10.1016/j.tree.2005.10.019 PMID: 16701459

54.

Cowan RS, Chase MW, Kress WJ, Savolainen V. 300,000 species to identify: problems, progress, and prospects in DNA barcoding of land plants. Taxon. 2006; 55(3):611–6. https://doi.org/10.2307/ 25065638

55.

Burgess KS, Fazekas AJ, Kesanakurti PR, Graham SW, Husband BC, Newmaster SG, et al. Discriminating plant species in a local temperate flora using the rbcL+matK DNA barcode. Methods Ecol Evol. 2011; 2(4):333–40. https://doi.org/10.1111/j.2041-210X.2011.00092.x

56.

Wiersema JH, Leon B. World economic plants: a standard reference: CRC press; 2013. ˘ LU M, Brummer EC. Clarifying the ploidy of some accessions in the USDA alfalfa germplasm ŞAKİROG collection. Turk J Bot. 2011; 35:509–19. https://doi.org/10.3906/bot-1002-47

57. 58.

Spooner DM. DNA barcoding will frequently fail in complicated groups: an example in wild potatoes. Am J Bot. 2009; 96(6):1177–89. https://doi.org/10.3732/ajb.0800246 PMID: 21628268


15 / 16


59.

Moon BC, Kim WJ, Ji Y, Lee YM, Kang YM, Choi G. Molecular identification of the traditional herbal medicines, Arisaematis Rhizoma and Pinelliae Tuber, and common adulterants via universal DNA barcode sequences. Genet Molr Res. 2016; 15(1). https://doi.org/10.4238/gmr.15017064 PMID: 26909979

60.

Hajibabaei M, Janzen DH, Burns JM, Hallwachs W, Hebert PD. DNA barcodes distinguish species of tropical Lepidoptera. PNAS. 2006; 103(4):968–71. https://doi.org/10.1073/pnas.0510466103 PMID: 16418261


16 / 16

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